BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL2109110002?filename=Coletti2020.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL2109110002?filename=Coletti2020.cpsprimaryOK200Emilia ChenNon-curatedordinary differential equation modelProstate CancerL2V4https://www.ebi.ac.uk/biomodels/MODEL210911000232493951falseBioModelsSBMLModelsColetti2020 QSP model of prostate cancer immunotherapy2020MODEL2109110002Coletti R, Leonardelli L, Parolo S, Marchetti LColetti R32493951,
Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, systems pharmacology modeling provides a quantitative framework to test in silico the efficacy of new treatments and combination therapies. In this paper we present a new Quantitative Systems Pharmacology (QSP) model of prostate cancer immunotherapy, calibrated using data from pre-clinical experiments in prostate cancer mouse models. We developed the model by using Ordinary Differential Equations (ODEs) describing the tumor, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, the model presented here serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models.. 1, 10.
University of Trento, Department of mathematics, Trento, 38123, Italy.
Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy.emiliachen1@gmail.comUniversity of CambridgeImmunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, systems pharmacology modeling provides a quantitative framework to test in silico the efficacy of new treatments and combination therapies. In this paper we present a new Quantitative Systems Pharmacology (QSP) model of prostate cancer immunotherapy, calibrated using data from pre-clinical experiments in prostate cancer mouse models. We developed the model by using Ordinary Differential Equations (ODEs) describing the tumor, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, the model presented here serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models.A QSP model of prostate cancer immunotherapy to identify effective combination therapies.Coletti Roberta R, Leonardelli Lorena L, Parolo Silvia S, Marchetti Luca Lmalignant neoplasm of the prostate, hereditary prostate cancer, malignant prostate tumor, cancer of the prostate, prostatic neoplasm, prostatic cancer, tumor of the prostate, carcinoma of prostate gland, familial, malignant tumour of prostate, malignant prostate gland neoplasm, carcinoma of the prostate, prostate carcinoma., NGP - new growth of prostate, cancer of prostate, carcinoma of prostate, malignant tumour of the prostate, malignant neoplasm of prostate, prostate gland carcinoma, cancer of prostate gland, malignant prostate neoplasm, malignant prostate tumour, malignant tumor of prostate, malignant neoplasm of prostate gland, prostate cancer, prostate gland cancer, NOS, PC, tumour of the prostate, prostate neoplasm, malignant tumor of the prostatemalignant neoplasm of the prostate, hereditary prostate cancer, malignant prostate tumor, cancer of the prostate, prostatic neoplasm, prostatic cancer, tumor of the prostate, carcinoma of prostate gland, familial, malignant tumour of prostate, malignant prostate gland neoplasm, carcinoma of the prostate, prostate carcinoma., NGP - new growth of prostate, cancer of prostate, carcinoma of prostate, malignant tumour of the prostate, malignant neoplasm of prostate, prostate gland carcinoma, cancer of prostate gland, malignant prostate neoplasm, malignant prostate tumour, malignant tumor of prostate, malignant neoplasm of prostate gland, prostate cancer, prostate gland cancer, NOS, PC, tumour of the prostate, prostate neoplasm, malignant tumor of the prostateandrogeno, malignant Growth, CPD photolyase activity, mode of action, androgens, advanced, pharmacodynamics, insensitive, Immune Systems, degree (angle), PhrB photolyase activity, carcinoma of prostate gland, cell type cancer, number, malignant prostate gland neoplasm, carcinoma of the prostate, Combination, Development, Medication, Tumor, photoreactivating enzyme activity, NGP - new growth of prostate, pigmented epithelium, androgene, Pharmaceutical Development, presence, dIKK-gamma, deoxyribodipyrimidine photolyase activity, stratum pigmentosum retinae, prostate gland carcinoma, Combinations, malignant prostate neoplasm, DmIKK-gamma, resistance, malignant tumor of prostate, 3, stratum pigmentosa retinae, NUP96, dmIKKgamma, outer pigmented layer of retina, IKK[[gamma]], Drug Combination, Drug Target Prediction, epithelium, neoplasm, prostate neoplasm, IKKg, malignant tumour, Gonadectomies, KEY, Key, treatment, Immune Processes, Immune Responses, MOS3, deoxyribocyclobutadipyrimidine pyrimidine-lyase activity, PRECOCIOUS, neoplasm (disease), malignant tumour of prostate, pigmented retina, F23A5.3, Medication Development, present in organism, carcinoma of prostate, DNA cyclobutane dipyrimidine photolyase activity, CA, SUPPRESSOR OF AUXIN RESISTANCE 3, Decision Tree, pigment epithelium of retina, Immune Response, Immune, mechanism of action, IKK, malignant neoplasm, Pharmaceutical, Decision, disease management, Therapies, pigmented retina epithelium, prostate gland cancer, house mouse, Pharmacologies, Immune Process, HHT1, F23A5_3, hereditary prostate cancer, Therapy, F23A5_3., PRE, cancer of the prostate, data, malignancy, prostatic neoplasm, prostatic cancer, tumor of the prostate, Papers, Process, malignant, deoxyribonucleic cyclobutane dipyrimidine photolyase activity, familial, mouse, pigmented retinal epithelium, Tree, androgenos, results, predicted, Trees, IKKgamma, cancer of prostate gland, count in organism, stratum pigmentosum (retina), DmIKKgamma, Androgen, MODIFIER OF SNC1, MT, Prediction, retinal pigment, Mus, dIKK, Androgene, malignant neoplasm (disease), Systems, deoxyribonucleic photolyase activity, Kenny, organ system cancer, precocious, malignant neoplasm of prostate gland, dipyrimidine photolyase (photosensitive), NOS, tumour of the prostate, END, retinal pigment layer, prostate carcinoma, Target Prediction, Drug Target Predictions, Gonadectomy, malignant neoplasm of the prostate, malignant prostate tumor, Castrations, primary cancer, RPE, Immunotherapies, resistant, photolyase activity, Dmikkgamma, retinal pigmented epithelium, mice, System, IKK-gamma, androgenes, arc degree, CG16910, cancer of prostate, retinal pigment epithelium, Treatments, malignant tumor, early, malignant tumour of the prostate, p. pigmentosa retinae, Drug, malignant neoplasm of prostate, Computational Prediction of Drug-Target Interactions, phr A photolyase activity, DNA-photoreactivating enzyme, DmelCG16910, pharmacologic action, Therapeutic, malignant neoplastic disease, Vaccine, malignant prostate tumour, Drug Target, Response, prostate cancer, PC, Treatment, deoxyribonucleate pyrimidine dimer lyase (photosensitive), Mouse, quantitative, ORW1, cancer, malignant tumor of the prostate, presence or absence in organismTraumatic Myelopathy, Spinal Cord Transection, D-jun/Jra, Rab interactor activity, F5E6_13, carcinoma of prostate gland, P62, AP-1, d-jun, cJun, ADP-ribosylation factor binding, Ral GTPase binding, l(2)46Ef, malignant prostate gland neoplasm, carcinoma of the prostate, sci, jra, NGP - new growth of prostate, F5E6.13, DOI, Publication., prostate gland carcinoma, malignant prostate neoplasm, Publication, malignant tumor of prostate, HOW, How, GTP-Rho binding, DmelCG8432, dJRA, dJra, prostate neoplasm, l(3)j5D5, doi, 24B, Junc, l(3)s2612, Reptin, l(2)IA109, REPT, dJun, dJUN, malignant tumour of prostate, stru, V, l(3)S053606, DmelCG9750, CG10293, carcinoma of prostate, Ran protein binding, l(3)j5B5, Jun, JUN, DmelCG2275, Table, DmelCG10293, c-Jun, prostate gland cancer, l(2R)IA109, species, Viewpoint, Editorial Comment, Spinal Cord Laceration, Rab GTPase binding, REP, hereditary prostate cancer, jun, cancer of the prostate, 0904/17, prostatic neoplasm, d-JRA, prostatic cancer, Spinal Cord Trauma, tumor of the prostate, Rab escort protein, D-Jun, clone 2.39, c-jun, familial, drep, AthREP, qkr, ARF binding, l(3)S090417, Tip48/Reptin, rep, Djun, Commentary, cancer of prostate gland, Rab escort protein activity, dm-Jun, SZ1, KH93F, Ras interactor activity, D-jun, malignant neoplasm of prostate gland, djun, Rept, NOS, Spinal Cord Injuries, dRep, Ran GTPase binding, tumour of the prostate, prostate carcinoma, anon-WO0142479.1, who, DJUN, DJun, malignant neoplasm of the prostate, dReptin, malignant prostate tumor, l(3)06945, CG9750, CG2275, Who/How, Ran-binding protein, Rho GTPase binding, cancer of prostate, malignant tumour of the prostate, malignant neoplasm of prostate, AP1, Post-Traumatic Myelopathy, Rac GTPase binding, CG8432, dAP-1, GTP-Ral binding, malignant prostate tumour, d-Jun, Tip48, Manuscripts, qkr[93F], prostate cancer, PC, anon-EST:Liang-2.39, Ras GTPase binding, SCI, JRA, malignant tumor of the prostate, Spinal Cord ContusionfalseColetti2020 - QSP model of prostate cancer immunotherapy
This model is based on the publication:
Coletti R, Leonardelli L, Parolo S, Marchetti L. A QSP model of prostate cancer immunotherapy to identify effective combination therapies. Sci Rep. 2020 Jun 3;10(1):9063. doi: 10.1038/s41598-020-65590-0
Comment:
Eq. 3 - no listed species "R", therefore based on Eq. 1 and 2, R_2 used instead.
Eq. 12 - no listed parameter "a_Df" in Table S1, a_Dc used based on parameter description.
Curation Comment:
Simulations are not reproducing manuscript plots. Matlab code is available (Supplementary File 2 of the manuscript publication) but has not been tested, so would be worth reattempting model curation.
2021-09-112021-09-112021-09-11MODEL210911000232493951C15438C94604GO:00028379606